## Overview
The RoBERTa model was proposed in RoBERTa: A Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. It is based on Googleβs BERT model released in 2018.
It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining objective and training with much larger mini-batches and learning rates.
The abstract from the paper is the following:
Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code.
This model was contributed by julien-c. The original code can be found here.
Usage tipsThis implementation is the same as BertModel with a minor tweak to the embeddings, as well as a setup for RoBERTa pretrained models.
RoBERTa has the same architecture as BERT but uses a byte-level BPE as a tokenizer (same as GPT-2) and uses a different pretraining scheme.
RoBERTa doesnβt have token_type_ids, so you donβt need to indicate which token belongs to which segment. Just separate your segments with the separation token tokenizer.sep_token (or </s>).
RoBERTa is similar to BERT but with better pretraining techniques:
CamemBERT is a wrapper around RoBERTa. Refer to its model page for usage examples.
A list of official Hugging Face and community (indicated by π) resources to help you get started with RoBERTa. If youβre interested in submitting a resource to be included here, please feel free to open a Pull Request and weβll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.
Text Classification
Token Classification
Fill-Mask
Question Answering
Multiple choice
( vocab_size = 50265 hidden_size = 768 num_hidden_layers = 12 num_attention_heads = 12 intermediate_size = 3072 hidden_act = 'gelu' hidden_dropout_prob = 0.1 attention_probs_dropout_prob = 0.1 max_position_embeddings = 512 type_vocab_size = 2 initializer_range = 0.02 layer_norm_eps = 1e-12 pad_token_id = 1 bos_token_id = 0 eos_token_id = 2 position_embedding_type = 'absolute' use_cache = True classifier_dropout = None **kwargs )
Parameters
int, optional, defaults to 50265) β Vocabulary size of the RoBERTa model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling RobertaModel or TFRobertaModel. int, optional, defaults to 768) β Dimensionality of the encoder layers and the pooler layer. int, optional, defaults to 12) β Number of hidden layers in the Transformer encoder. int, optional, defaults to 12) β Number of attention heads for each attention layer in the Transformer encoder. int, optional, defaults to 3072) β Dimensionality of the βintermediateβ (often named feed-forward) layer in the Transformer encoder. str or Callable, optional, defaults to "gelu") β The non-linear activation function (function or string) in the encoder and pooler. If string, "gelu", "relu", "silu" and "gelu_new" are supported. float, optional, defaults to 0.1) β The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. float, optional, defaults to 0.1) β The dropout ratio for the attention probabilities. int, optional, defaults to 512) β The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). int, optional, defaults to 2) β The vocabulary size of the token_type_ids passed when calling RobertaModel or TFRobertaModel. float, optional, defaults to 0.02) β The standard deviation of the truncated_normal_initializer for initializing all weight matrices. float, optional, defaults to 1e-12) β The epsilon used by the layer normalization layers. str, optional, defaults to "absolute") β Type of position embedding. Choose one of "absolute", "relative_key", "relative_key_query". For positional embeddings use "absolute". For more information on "relative_key", please refer to Self-Attention with Relative Position Representations (Shaw et al.). For more information on "relative_key_query", please refer to Method 4 in Improve Transformer Models with Better Relative Position Embeddings (Huang et al.). bool, optional, defaults to False) β Whether the model is used as a decoder or not. If False, the model is used as an encoder. bool, optional, defaults to True) β Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if config.is_decoder=True. float, optional) β The dropout ratio for the classification head. This is the configuration class to store the configuration of a RobertaModel or a TFRobertaModel. It is used to instantiate a RoBERTa model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the RoBERTa FacebookAI/roberta-base architecture.
Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.
Examples:
>>> from transformers import RobertaConfig, RobertaModel >>> >>> configuration = RobertaConfig() >>> >>> model = RobertaModel(configuration) >>> >>> configuration = model.configRobertaTokenizer class transformers.RobertaTokenizer < source >
( vocab_file merges_file errors = 'replace' bos_token = '<s>' eos_token = '</s>' sep_token = '</s>' cls_token = '<s>' unk_token = '<unk>' pad_token = '<pad>' mask_token = '<mask>' add_prefix_space = False **kwargs )
Parameters
str) β Path to the vocabulary file. str) β Path to the merges file. str, optional, defaults to "replace") β Paradigm to follow when decoding bytes to UTF-8. See bytes.decode for more information. str, optional, defaults to "<s>") β The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the cls_token.
str, optional, defaults to "</s>") β The end of sequence token.
When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the sep_token.
str, optional, defaults to "</s>") β The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. str, optional, defaults to "<s>") β The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. str, optional, defaults to "<unk>") β The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. str, optional, defaults to "<pad>") β The token used for padding, for example when batching sequences of different lengths. str, optional, defaults to "<mask>") β The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. bool, optional, defaults to False) β Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (RoBERTa tokenizer detect beginning of words by the preceding space). Constructs a RoBERTa tokenizer, derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
>>> from transformers import RobertaTokenizer
>>> tokenizer = RobertaTokenizer.from_pretrained("FacebookAI/roberta-base")
>>> tokenizer("Hello world")["input_ids"]
[0, 31414, 232, 2]
>>> tokenizer(" Hello world")["input_ids"]
[0, 20920, 232, 2]
You can get around that behavior by passing add_prefix_space=True when instantiating this tokenizer or when you call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
When used with is_split_into_words=True, this tokenizer will add a space before each word (even the first one).
This tokenizer inherits from PreTrainedTokenizer which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.
build_inputs_with_special_tokens < source >( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) β List[int]
Parameters
List[int]) β List of IDs to which the special tokens will be added. List[int], optional) β Optional second list of IDs for sequence pairs. List of input IDs with the appropriate special tokens.
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens. A RoBERTa sequence has the following format:
<s> X </s><s> A </s></s> B </s>( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None already_has_special_tokens: bool = False ) β List[int]
Parameters
List[int]) β List of IDs. List[int], optional) β Optional second list of IDs for sequence pairs. bool, optional, defaults to False) β Whether or not the token list is already formatted with special tokens for the model. A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer prepare_for_model method.
( token_ids_0: typing.List[int] token_ids_1: typing.Optional[typing.List[int]] = None ) β List[int]
Parameters
List[int]) β List of IDs. List[int], optional) β Optional second list of IDs for sequence pairs. List of zeros.
Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not make use of token type ids, therefore a list of zeros is returned.
save_vocabulary < source >( save_directory: str filename_prefix: typing.Optional[str] = None )
RobertaTokenizerFast class transformers.RobertaTokenizerFast < source >( vocab_file = None merges_file = None tokenizer_file = None errors = 'replace' bos_token = '<s>' eos_token = '</s>' sep_token = '</s>' cls_token = '<s>' unk_token = '<unk>' pad_token = '<pad>' mask_token = '<mask>' add_prefix_space = False trim_offsets = True **kwargs )
Parameters
str) β Path to the vocabulary file. str) β Path to the merges file. str, optional, defaults to "replace") β Paradigm to follow when decoding bytes to UTF-8. See bytes.decode for more information. str, optional, defaults to "<s>") β The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
When building a sequence using special tokens, this is not the token that is used for the beginning of sequence. The token used is the cls_token.
str, optional, defaults to "</s>") β The end of sequence token.
When building a sequence using special tokens, this is not the token that is used for the end of sequence. The token used is the sep_token.
str, optional, defaults to "</s>") β The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for sequence classification or for a text and a question for question answering. It is also used as the last token of a sequence built with special tokens. str, optional, defaults to "<s>") β The classifier token which is used when doing sequence classification (classification of the whole sequence instead of per-token classification). It is the first token of the sequence when built with special tokens. str, optional, defaults to "<unk>") β The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. str, optional, defaults to "<pad>") β The token used for padding, for example when batching sequences of different lengths. str, optional, defaults to "<mask>") β The token used for masking values. This is the token used when training this model with masked language modeling. This is the token which the model will try to predict. bool, optional, defaults to False) β Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word. (RoBERTa tokenizer detect beginning of words by the preceding space). bool, optional, defaults to True) β Whether the post processing step should trim offsets to avoid including whitespaces. Construct a βfastβ RoBERTa tokenizer (backed by HuggingFaceβs tokenizers library), derived from the GPT-2 tokenizer, using byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
>>> from transformers import RobertaTokenizerFast
>>> tokenizer = RobertaTokenizerFast.from_pretrained("FacebookAI/roberta-base")
>>> tokenizer("Hello world")["input_ids"]
[0, 31414, 232, 2]
>>> tokenizer(" Hello world")["input_ids"]
[0, 20920, 232, 2]
You can get around that behavior by passing add_prefix_space=True when instantiating this tokenizer or when you call it on some text, but since the model was not pretrained this way, it might yield a decrease in performance.
When used with is_split_into_words=True, this tokenizer needs to be instantiated with add_prefix_space=True.
This tokenizer inherits from PreTrainedTokenizerFast which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.
build_inputs_with_special_tokens < source >( token_ids_0 token_ids_1 = None )
Pytorch
Hide Pytorch content
RobertaModel class transformers.RobertaModel < source >( config add_pooling_layer = True )
Parameters
The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in Attention is all you need by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
To behave as an decoder the model needs to be initialized with the is_decoder argument of the configuration set to True. To be used in a Seq2Seq model, the model needs to initialized with both is_decoder argument and add_cross_attention set to True; an encoder_hidden_states is then expected as an input to the forward pass.
( input_ids: typing.Optional[torch.Tensor] = None attention_mask: typing.Optional[torch.Tensor] = None token_type_ids: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.Tensor] = None head_mask: typing.Optional[torch.Tensor] = None inputs_embeds: typing.Optional[torch.Tensor] = None encoder_hidden_states: typing.Optional[torch.Tensor] = None encoder_attention_mask: typing.Optional[torch.Tensor] = None past_key_values: typing.Optional[typing.List[torch.FloatTensor]] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) β transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or tuple(torch.FloatTensor)
Parameters
torch.LongTensor of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
torch.LongTensor of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0,1]:
type_vocab_size parameter with value
= 2. All the value in this tensor should be always < type_vocab_size.
torch.LongTensor of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) β Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) β Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the modelβs internal embedding lookup matrix. bool, optional) β Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. bool, optional) β Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) β Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. torch.FloatTensor of shape (batch_size, sequence_length) or (batch_size, sequence_length, target_length), optional) β Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:
tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) β Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that donβt have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).
bool, optional) β If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values). A transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) β Sequence of hidden-states at the output of the last layer of the model.
pooler_output (torch.FloatTensor of shape (batch_size, hidden_size)) β Last layer hidden-state of the first token of the sequence (classification token) after further processing through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns the classification token after processing through a linear layer and a tanh activation function. The linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True and config.add_cross_attention=True is passed or when config.output_attentions=True) β Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoderβs cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) β Tuple of tuple(torch.FloatTensor) of length config.n_layers, with each tuple having 2 tensors of shape (batch_size, num_heads, sequence_length, embed_size_per_head)) and optionally if config.is_encoder_decoder=True 2 additional tensors of shape (batch_size, num_heads, encoder_sequence_length, embed_size_per_head).
Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
The RobertaModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, RobertaModel
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
>>> model = RobertaModel.from_pretrained("FacebookAI/roberta-base")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state RobertaForCausalLM class transformers.RobertaForCausalLM < source >
( config )
Parameters
RoBERTa Model with a language modeling head on top for CLM fine-tuning.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward < source >( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None encoder_hidden_states: typing.Optional[torch.FloatTensor] = None encoder_attention_mask: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None past_key_values: typing.Tuple[typing.Tuple[torch.FloatTensor]] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None **kwargs ) β transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
Parameters
torch.LongTensor of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
torch.LongTensor of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0,1]:
type_vocab_size parameter with value
= 2. All the value in this tensor should be always < type_vocab_size.
torch.LongTensor of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) β Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) β Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the modelβs internal embedding lookup matrix. bool, optional) β Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. bool, optional) β Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) β Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. torch.FloatTensor of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:
torch.LongTensor of shape (batch_size, sequence_length), optional) β Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size] tuple(tuple(torch.FloatTensor)) of length config.n_layers with each tuple having 4 tensors of shape (batch_size, num_heads, sequence_length - 1, embed_size_per_head)) β Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that donβt have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length).
bool, optional) β If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values). A transformers.modeling_outputs.CausalLMOutputWithCrossAttentions or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) β Language modeling loss (for next-token prediction).
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads.
past_key_values (tuple(tuple(torch.FloatTensor)), optional, returned when use_cache=True is passed or when config.use_cache=True) β Tuple of torch.FloatTensor tuples of length config.n_layers, with each tuple containing the cached key, value states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. Only relevant if config.is_decoder = True.
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
The RobertaForCausalLM forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, RobertaForCausalLM, AutoConfig
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
>>> config = AutoConfig.from_pretrained("FacebookAI/roberta-base")
>>> config.is_decoder = True
>>> model = RobertaForCausalLM.from_pretrained("FacebookAI/roberta-base", config=config)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logits RobertaForMaskedLM class transformers.RobertaForMaskedLM < source >
( config )
Parameters
RoBERTa Model with a language modeling head on top.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward < source >( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None encoder_hidden_states: typing.Optional[torch.FloatTensor] = None encoder_attention_mask: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) β transformers.modeling_outputs.MaskedLMOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
torch.LongTensor of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0,1]:
type_vocab_size parameter with value
= 2. All the value in this tensor should be always < type_vocab_size.
torch.LongTensor of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) β Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) β Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the modelβs internal embedding lookup matrix. bool, optional) β Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. bool, optional) β Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. torch.LongTensor of shape (batch_size, sequence_length), optional) β Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size] Dict[str, any], optional, defaults to {}) β Used to hide legacy arguments that have been deprecated. A transformers.modeling_outputs.MaskedLMOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) β Masked language modeling (MLM) loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The RobertaForMaskedLM forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, RobertaForMaskedLM
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
>>> model = RobertaForMaskedLM.from_pretrained("FacebookAI/roberta-base")
>>> inputs = tokenizer("The capital of France is <mask>.", return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>>
>>> mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]
>>> predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
>>> tokenizer.decode(predicted_token_id)
' Paris'
>>> labels = tokenizer("The capital of France is Paris.", return_tensors="pt")["input_ids"]
>>>
>>> labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
>>> outputs = model(**inputs, labels=labels)
>>> round(outputs.loss.item(), 2)
0.1 RobertaForSequenceClassification class transformers.RobertaForSequenceClassification < source >
( config )
Parameters
RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward < source >( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) β transformers.modeling_outputs.SequenceClassifierOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
torch.LongTensor of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0,1]:
type_vocab_size parameter with value
= 2. All the value in this tensor should be always < type_vocab_size.
torch.LongTensor of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) β Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) β Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the modelβs internal embedding lookup matrix. bool, optional) β Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. bool, optional) β Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. torch.LongTensor of shape (batch_size,), optional) β Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If config.num_labels > 1 a classification loss is computed (Cross-Entropy). A transformers.modeling_outputs.SequenceClassifierOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) β Classification (or regression if config.num_labels==1) loss.
logits (torch.FloatTensor of shape (batch_size, config.num_labels)) β Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The RobertaForSequenceClassification forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example of single-label classification:
>>> import torch
>>> from transformers import AutoTokenizer, RobertaForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
>>> model = RobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_class_id = logits.argmax().item()
>>> model.config.id2label[predicted_class_id]
'optimism'
>>>
>>> num_labels = len(model.config.id2label)
>>> model = RobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion", num_labels=num_labels)
>>> labels = torch.tensor([1])
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
0.08
Example of multi-label classification:
>>> import torch
>>> from transformers import AutoTokenizer, RobertaForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
>>> model = RobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion", problem_type="multi_label_classification")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_class_ids = torch.arange(0, logits.shape[-1])[torch.sigmoid(logits).squeeze(dim=0) > 0.5]
>>>
>>> num_labels = len(model.config.id2label)
>>> model = RobertaForSequenceClassification.from_pretrained(
... "cardiffnlp/twitter-roberta-base-emotion", num_labels=num_labels, problem_type="multi_label_classification"
... )
>>> labels = torch.sum(
... torch.nn.functional.one_hot(predicted_class_ids[None, :].clone(), num_classes=num_labels), dim=1
... ).to(torch.float)
>>> loss = model(**inputs, labels=labels).loss RobertaForMultipleChoice class transformers.RobertaForMultipleChoice < source >
( config )
Parameters
Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward < source >( input_ids: typing.Optional[torch.LongTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) β transformers.modeling_outputs.MultipleChoiceModelOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor of shape (batch_size, num_choices, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor of shape (batch_size, num_choices, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
torch.LongTensor of shape (batch_size, num_choices, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0,1]:
type_vocab_size parameter with value
= 2. All the value in this tensor should be always < type_vocab_size.
torch.LongTensor of shape (batch_size, num_choices, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) β Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
torch.FloatTensor of shape (batch_size, num_choices, sequence_length, hidden_size), optional) β Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the modelβs internal embedding lookup matrix. bool, optional) β Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. bool, optional) β Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. torch.LongTensor of shape (batch_size,), optional) β Labels for computing the multiple choice classification loss. Indices should be in [0, ..., num_choices-1] where num_choices is the size of the second dimension of the input tensors. (See input_ids above) A transformers.modeling_outputs.MultipleChoiceModelOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) β Classification loss.
logits (torch.FloatTensor of shape (batch_size, num_choices)) β num_choices is the second dimension of the input tensors. (see input_ids above).
Classification scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The RobertaForMultipleChoice forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, RobertaForMultipleChoice
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
>>> model = RobertaForMultipleChoice.from_pretrained("FacebookAI/roberta-base")
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."
>>> labels = torch.tensor(0).unsqueeze(0)
>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="pt", padding=True)
>>> outputs = model(**{k: v.unsqueeze(0) for k, v in encoding.items()}, labels=labels)
>>>
>>> loss = outputs.loss
>>> logits = outputs.logits RobertaForTokenClassification class transformers.RobertaForTokenClassification < source >
( config )
Parameters
Roberta Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward < source >( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) β transformers.modeling_outputs.TokenClassifierOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
torch.LongTensor of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0,1]:
type_vocab_size parameter with value
= 2. All the value in this tensor should be always < type_vocab_size.
torch.LongTensor of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) β Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) β Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the modelβs internal embedding lookup matrix. bool, optional) β Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. bool, optional) β Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. torch.LongTensor of shape (batch_size, sequence_length), optional) β Labels for computing the token classification loss. Indices should be in [0, ..., config.num_labels - 1]. A transformers.modeling_outputs.TokenClassifierOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) β Classification loss.
logits (torch.FloatTensor of shape (batch_size, sequence_length, config.num_labels)) β Classification scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The RobertaForTokenClassification forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, RobertaForTokenClassification
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/roberta-large-ner-english")
>>> model = RobertaForTokenClassification.from_pretrained("Jean-Baptiste/roberta-large-ner-english")
>>> inputs = tokenizer(
... "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="pt"
... )
>>> with torch.no_grad():
... logits = model(**inputs).logits
>>> predicted_token_class_ids = logits.argmax(-1)
>>>
>>>
>>>
>>> predicted_tokens_classes = [model.config.id2label[t.item()] for t in predicted_token_class_ids[0]]
>>> predicted_tokens_classes
['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']
>>> labels = predicted_token_class_ids
>>> loss = model(**inputs, labels=labels).loss
>>> round(loss.item(), 2)
0.01 RobertaForQuestionAnswering class transformers.RobertaForQuestionAnswering < source >
( config )
Parameters
Roberta Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute span start logits and span end logits).
This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.
forward < source >( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.FloatTensor] = None token_type_ids: typing.Optional[torch.LongTensor] = None position_ids: typing.Optional[torch.LongTensor] = None head_mask: typing.Optional[torch.FloatTensor] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None start_positions: typing.Optional[torch.LongTensor] = None end_positions: typing.Optional[torch.LongTensor] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None ) β transformers.modeling_outputs.QuestionAnsweringModelOutput or tuple(torch.FloatTensor)
Parameters
torch.LongTensor of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
torch.FloatTensor of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
torch.LongTensor of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0,1]:
type_vocab_size parameter with value
= 2. All the value in this tensor should be always < type_vocab_size.
torch.LongTensor of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
torch.FloatTensor of shape (num_heads,) or (num_layers, num_heads), optional) β Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) β Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the modelβs internal embedding lookup matrix. bool, optional) β Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. bool, optional) β Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. torch.LongTensor of shape (batch_size,), optional) β Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss. torch.LongTensor of shape (batch_size,), optional) β Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss. A transformers.modeling_outputs.QuestionAnsweringModelOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) β Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (torch.FloatTensor of shape (batch_size, sequence_length)) β Span-start scores (before SoftMax).
end_logits (torch.FloatTensor of shape (batch_size, sequence_length)) β Span-end scores (before SoftMax).
hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The RobertaForQuestionAnswering forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, RobertaForQuestionAnswering
>>> import torch
>>> tokenizer = AutoTokenizer.from_pretrained("deepset/roberta-base-squad2")
>>> model = RobertaForQuestionAnswering.from_pretrained("deepset/roberta-base-squad2")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="pt")
>>> with torch.no_grad():
... outputs = model(**inputs)
>>> answer_start_index = outputs.start_logits.argmax()
>>> answer_end_index = outputs.end_logits.argmax()
>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> tokenizer.decode(predict_answer_tokens, skip_special_tokens=True)
' puppet'
>>>
>>> target_start_index = torch.tensor([14])
>>> target_end_index = torch.tensor([15])
>>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
>>> loss = outputs.loss
>>> round(loss.item(), 2)
0.86
TensorFlow
Hide TensorFlow content
TFRobertaModel class transformers.TFRobertaModel < source >( config *inputs **kwargs )
Parameters
The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers accept two formats as input:
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should βjust workβ for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:
input_ids only and nothing else: model(input_ids)model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])model({"input_ids": input_ids, "token_type_ids": token_type_ids})Note that when creating models and layers with subclassing then you donβt need to worry about any of this, as you can just pass inputs like you would to any other Python function!
call < source >( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None position_ids: np.ndarray | tf.Tensor | None = None head_mask: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None encoder_hidden_states: np.ndarray | tf.Tensor | None = None encoder_attention_mask: np.ndarray | tf.Tensor | None = None past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None use_cache: Optional[bool] = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None training: Optional[bool] = False ) β transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions or tuple(tf.Tensor)
Parameters
Numpy array or tf.Tensor of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
Numpy array or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) β Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) β Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the modelβs internal embedding lookup matrix. bool, optional) β Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. bool, optional) β Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. bool, optional, defaults to False) β Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) β Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. tf.Tensor of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:
Tuple[Tuple[tf.Tensor]] of length config.n_layers) β contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that donβt have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length). bool, optional, defaults to True) β If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values). Set to False during training, True during generation A transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
last_hidden_state (tf.Tensor of shape (batch_size, sequence_length, hidden_size)) β Sequence of hidden-states at the output of the last layer of the model.
pooler_output (tf.Tensor of shape (batch_size, hidden_size)) β Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.
This output is usually not a good summary of the semantic content of the input, youβre often better with averaging or pooling the sequence of hidden-states for the whole input sequence.
past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) β List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoderβs cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
The TFRobertaModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, TFRobertaModel
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
>>> model = TFRobertaModel.from_pretrained("FacebookAI/roberta-base")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)
>>> last_hidden_states = outputs.last_hidden_state TFRobertaForCausalLM class transformers.TFRobertaForCausalLM < source >
( config: RobertaConfig *inputs **kwargs )
call < source >( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None position_ids: np.ndarray | tf.Tensor | None = None head_mask: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None encoder_hidden_states: np.ndarray | tf.Tensor | None = None encoder_attention_mask: np.ndarray | tf.Tensor | None = None past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None use_cache: Optional[bool] = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None labels: np.ndarray | tf.Tensor | None = None training: Optional[bool] = False ) β transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions or tuple(tf.Tensor)
Parameters
Numpy array or tf.Tensor of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
Numpy array or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) β Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) β Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the modelβs internal embedding lookup matrix. bool, optional) β Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. bool, optional) β Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. bool, optional, defaults to False) β Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) β Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. tf.Tensor of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in [0, 1]:
Tuple[Tuple[tf.Tensor]] of length config.n_layers) β contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If past_key_values are used, the user can optionally input only the last decoder_input_ids (those that donβt have their past key value states given to this model) of shape (batch_size, 1) instead of all decoder_input_ids of shape (batch_size, sequence_length). bool, optional, defaults to True) β If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values). Set to False during training, True during generation tf.Tensor or np.ndarray of shape (batch_size, sequence_length), optional) β Labels for computing the cross entropy classification loss. Indices should be in [0, ..., config.vocab_size - 1]. A transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) β Language modeling loss (for next-token prediction).
logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights of the decoderβs cross-attention layer, after the attention softmax, used to compute the weighted average in the cross-attention heads.
past_key_values (List[tf.Tensor], optional, returned when use_cache=True is passed or when config.use_cache=True) β List of tf.Tensor of length config.n_layers, with each tensor of shape (2, batch_size, num_heads, sequence_length, embed_size_per_head)).
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
The TFRobertaForCausalLM forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, TFRobertaForCausalLM
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
>>> model = TFRobertaForCausalLM.from_pretrained("FacebookAI/roberta-base")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> outputs = model(inputs)
>>> logits = outputs.logits TFRobertaForMaskedLM class transformers.TFRobertaForMaskedLM < source >
( config *inputs **kwargs )
Parameters
RoBERTa Model with a language modeling head on top.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers accept two formats as input:
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should βjust workβ for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:
input_ids only and nothing else: model(input_ids)model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])model({"input_ids": input_ids, "token_type_ids": token_type_ids})Note that when creating models and layers with subclassing then you donβt need to worry about any of this, as you can just pass inputs like you would to any other Python function!
call < source >( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None position_ids: np.ndarray | tf.Tensor | None = None head_mask: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None labels: np.ndarray | tf.Tensor | None = None training: Optional[bool] = False ) β transformers.modeling_tf_outputs.TFMaskedLMOutput or tuple(tf.Tensor)
Parameters
Numpy array or tf.Tensor of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
Numpy array or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) β Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) β Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the modelβs internal embedding lookup matrix. bool, optional) β Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. bool, optional) β Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. bool, optional, defaults to False) β Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). tf.Tensor of shape (batch_size, sequence_length), optional) β Labels for computing the masked language modeling loss. Indices should be in [-100, 0, ..., config.vocab_size] (see input_ids docstring) Tokens with indices set to -100 are ignored (masked), the loss is only computed for the tokens with labels in [0, ..., config.vocab_size] A transformers.modeling_tf_outputs.TFMaskedLMOutput or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
loss (tf.Tensor of shape (n,), optional, where n is the number of non-masked labels, returned when labels is provided) β Masked language modeling (MLM) loss.
logits (tf.Tensor of shape (batch_size, sequence_length, config.vocab_size)) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The TFRobertaForMaskedLM forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, TFRobertaForMaskedLM
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
>>> model = TFRobertaForMaskedLM.from_pretrained("FacebookAI/roberta-base")
>>> inputs = tokenizer("The capital of France is <mask>.", return_tensors="tf")
>>> logits = model(**inputs).logits
>>>
>>> mask_token_index = tf.where((inputs.input_ids == tokenizer.mask_token_id)[0])
>>> selected_logits = tf.gather_nd(logits[0], indices=mask_token_index)
>>> predicted_token_id = tf.math.argmax(selected_logits, axis=-1)
>>> tokenizer.decode(predicted_token_id)
' Paris'
>>> labels = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"]
>>>
>>> labels = tf.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100)
>>> outputs = model(**inputs, labels=labels)
>>> round(float(outputs.loss), 2)
0.1 TFRobertaForSequenceClassification class transformers.TFRobertaForSequenceClassification < source >
( config *inputs **kwargs )
Parameters
RoBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers accept two formats as input:
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should βjust workβ for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:
input_ids only and nothing else: model(input_ids)model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])model({"input_ids": input_ids, "token_type_ids": token_type_ids})Note that when creating models and layers with subclassing then you donβt need to worry about any of this, as you can just pass inputs like you would to any other Python function!
call < source >( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None position_ids: np.ndarray | tf.Tensor | None = None head_mask: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None labels: np.ndarray | tf.Tensor | None = None training: Optional[bool] = False ) β transformers.modeling_tf_outputs.TFSequenceClassifierOutput or tuple(tf.Tensor)
Parameters
Numpy array or tf.Tensor of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
Numpy array or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) β Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) β Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the modelβs internal embedding lookup matrix. bool, optional) β Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. bool, optional) β Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. bool, optional, defaults to False) β Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). tf.Tensor of shape (batch_size,), optional) β Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If config.num_labels > 1 a classification loss is computed (Cross-Entropy). A transformers.modeling_tf_outputs.TFSequenceClassifierOutput or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
loss (tf.Tensor of shape (batch_size, ), optional, returned when labels is provided) β Classification (or regression if config.num_labels==1) loss.
logits (tf.Tensor of shape (batch_size, config.num_labels)) β Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The TFRobertaForSequenceClassification forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, TFRobertaForSequenceClassification
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
>>> model = TFRobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
>>> logits = model(**inputs).logits
>>> predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])
>>> model.config.id2label[predicted_class_id]
'optimism'
>>>
>>> num_labels = len(model.config.id2label)
>>> model = TFRobertaForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-emotion", num_labels=num_labels)
>>> labels = tf.constant(1)
>>> loss = model(**inputs, labels=labels).loss
>>> round(float(loss), 2)
0.08 TFRobertaForMultipleChoice class transformers.TFRobertaForMultipleChoice < source >
( config *inputs **kwargs )
Parameters
Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers accept two formats as input:
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should βjust workβ for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:
input_ids only and nothing else: model(input_ids)model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])model({"input_ids": input_ids, "token_type_ids": token_type_ids})Note that when creating models and layers with subclassing then you donβt need to worry about any of this, as you can just pass inputs like you would to any other Python function!
call < source >( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None position_ids: np.ndarray | tf.Tensor | None = None head_mask: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None labels: np.ndarray | tf.Tensor | None = None training: Optional[bool] = False ) β transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput or tuple(tf.Tensor)
Parameters
Numpy array or tf.Tensor of shape (batch_size, num_choices, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
Numpy array or tf.Tensor of shape (batch_size, num_choices, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
Numpy array or tf.Tensor of shape (batch_size, num_choices, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
Numpy array or tf.Tensor of shape (batch_size, num_choices, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
Numpy array or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) β Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
tf.Tensor of shape (batch_size, num_choices, sequence_length, hidden_size), optional) β Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the modelβs internal embedding lookup matrix. bool, optional) β Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. bool, optional) β Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. bool, optional, defaults to False) β Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). tf.Tensor of shape (batch_size,), optional) β Labels for computing the multiple choice classification loss. Indices should be in [0, ..., num_choices] where num_choices is the size of the second dimension of the input tensors. (See input_ids above) A transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
loss (tf.Tensor of shape (batch_size, ), optional, returned when labels is provided) β Classification loss.
logits (tf.Tensor of shape (batch_size, num_choices)) β num_choices is the second dimension of the input tensors. (see input_ids above).
Classification scores (before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The TFRobertaForMultipleChoice forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, TFRobertaForMultipleChoice
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
>>> model = TFRobertaForMultipleChoice.from_pretrained("FacebookAI/roberta-base")
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."
>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="tf", padding=True)
>>> inputs = {k: tf.expand_dims(v, 0) for k, v in encoding.items()}
>>> outputs = model(inputs)
>>>
>>> logits = outputs.logits TFRobertaForTokenClassification class transformers.TFRobertaForTokenClassification < source >
( config *inputs **kwargs )
Parameters
RoBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers accept two formats as input:
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should βjust workβ for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:
input_ids only and nothing else: model(input_ids)model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])model({"input_ids": input_ids, "token_type_ids": token_type_ids})Note that when creating models and layers with subclassing then you donβt need to worry about any of this, as you can just pass inputs like you would to any other Python function!
call < source >( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None position_ids: np.ndarray | tf.Tensor | None = None head_mask: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None labels: np.ndarray | tf.Tensor | None = None training: Optional[bool] = False ) β transformers.modeling_tf_outputs.TFTokenClassifierOutput or tuple(tf.Tensor)
Parameters
Numpy array or tf.Tensor of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
Numpy array or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) β Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) β Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the modelβs internal embedding lookup matrix. bool, optional) β Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. bool, optional) β Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. bool, optional, defaults to False) β Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). tf.Tensor of shape (batch_size, sequence_length), optional) β Labels for computing the token classification loss. Indices should be in [0, ..., config.num_labels - 1]. A transformers.modeling_tf_outputs.TFTokenClassifierOutput or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
loss (tf.Tensor of shape (n,), optional, where n is the number of unmasked labels, returned when labels is provided) β Classification loss.
logits (tf.Tensor of shape (batch_size, sequence_length, config.num_labels)) β Classification scores (before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The TFRobertaForTokenClassification forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, TFRobertaForTokenClassification
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("ydshieh/roberta-large-ner-english")
>>> model = TFRobertaForTokenClassification.from_pretrained("ydshieh/roberta-large-ner-english")
>>> inputs = tokenizer(
... "HuggingFace is a company based in Paris and New York", add_special_tokens=False, return_tensors="tf"
... )
>>> logits = model(**inputs).logits
>>> predicted_token_class_ids = tf.math.argmax(logits, axis=-1)
>>>
>>>
>>>
>>> predicted_tokens_classes = [model.config.id2label[t] for t in predicted_token_class_ids[0].numpy().tolist()]
>>> predicted_tokens_classes
['O', 'ORG', 'ORG', 'O', 'O', 'O', 'O', 'O', 'LOC', 'O', 'LOC', 'LOC']
>>> labels = predicted_token_class_ids >>> loss = tf.math.reduce_mean(model(**inputs, labels=labels).loss) >>> round(float(loss), 2) 0.01TFRobertaForQuestionAnswering class transformers.TFRobertaForQuestionAnswering < source >
( config *inputs **kwargs )
Parameters
RoBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute span start logits and span end logits).
This model inherits from TFPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)
This model is also a keras.Model subclass. Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and behavior.
TensorFlow models and layers in transformers accept two formats as input:
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models and layers. Because of this support, when using methods like model.fit() things should βjust workβ for you - just pass your inputs and labels in any format that model.fit() supports! If, however, you want to use the second format outside of Keras methods like fit() and predict(), such as when creating your own layers or models with the Keras Functional API, there are three possibilities you can use to gather all the input Tensors in the first positional argument:
input_ids only and nothing else: model(input_ids)model([input_ids, attention_mask]) or model([input_ids, attention_mask, token_type_ids])model({"input_ids": input_ids, "token_type_ids": token_type_ids})Note that when creating models and layers with subclassing then you donβt need to worry about any of this, as you can just pass inputs like you would to any other Python function!
call < source >( input_ids: TFModelInputType | None = None attention_mask: np.ndarray | tf.Tensor | None = None token_type_ids: np.ndarray | tf.Tensor | None = None position_ids: np.ndarray | tf.Tensor | None = None head_mask: np.ndarray | tf.Tensor | None = None inputs_embeds: np.ndarray | tf.Tensor | None = None output_attentions: Optional[bool] = None output_hidden_states: Optional[bool] = None return_dict: Optional[bool] = None start_positions: np.ndarray | tf.Tensor | None = None end_positions: np.ndarray | tf.Tensor | None = None training: Optional[bool] = False ) β transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or tuple(tf.Tensor)
Parameters
Numpy array or tf.Tensor of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.call() and PreTrainedTokenizer.encode() for details.
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
Numpy array or tf.Tensor of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1].
Numpy array or tf.Tensor of shape (num_heads,) or (num_layers, num_heads), optional) β Mask to nullify selected heads of the self-attention modules. Mask values selected in [0, 1]:
tf.Tensor of shape (batch_size, sequence_length, hidden_size), optional) β Optionally, instead of passing input_ids you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert input_ids indices into associated vectors than the modelβs internal embedding lookup matrix. bool, optional) β Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. bool, optional) β Whether or not to return the hidden states of all layers. See hidden_states under returned tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the config will be used instead. bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. This argument can be used in eager mode, in graph mode the value will always be set to True. bool, optional, defaults to False) β Whether or not to use the model in training mode (some modules like dropout modules have different behaviors between training and evaluation). tf.Tensor of shape (batch_size,), optional) β Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss. tf.Tensor of shape (batch_size,), optional) β Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (sequence_length). Position outside of the sequence are not taken into account for computing the loss. A transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput or a tuple of tf.Tensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
loss (tf.Tensor of shape (batch_size, ), optional, returned when start_positions and end_positions are provided) β Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (tf.Tensor of shape (batch_size, sequence_length)) β Span-start scores (before SoftMax).
end_logits (tf.Tensor of shape (batch_size, sequence_length)) β Span-end scores (before SoftMax).
hidden_states (tuple(tf.Tensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of tf.Tensor (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(tf.Tensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of tf.Tensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The TFRobertaForQuestionAnswering forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, TFRobertaForQuestionAnswering
>>> import tensorflow as tf
>>> tokenizer = AutoTokenizer.from_pretrained("ydshieh/roberta-base-squad2")
>>> model = TFRobertaForQuestionAnswering.from_pretrained("ydshieh/roberta-base-squad2")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="tf")
>>> outputs = model(**inputs)
>>> answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
>>> answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])
>>> predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1]
>>> tokenizer.decode(predict_answer_tokens)
' puppet'
>>> >>> target_start_index = tf.constant([14]) >>> target_end_index = tf.constant([15]) >>> outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index) >>> loss = tf.math.reduce_mean(outputs.loss) >>> round(float(loss), 2) 0.86FlaxRobertaModel class transformers.FlaxRobertaModel < source >
( config: RobertaConfig input_shape: typing.Tuple = (1, 1) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True gradient_checkpointing: bool = False **kwargs )
Parameters
The bare RoBERTa Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a flax.linen.Module subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
__call__ < source >( input_ids attention_mask = None token_type_ids = None position_ids = None head_mask = None encoder_hidden_states = None encoder_attention_mask = None params: dict = None dropout_rng: <function PRNGKey at 0x7fc27ba0dd80> = None train: bool = False output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None past_key_values: dict = None ) β transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling or tuple(torch.FloatTensor)
Parameters
numpy.ndarray of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
numpy.ndarray of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
numpy.ndarray of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
numpy.ndarray of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]. numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:
bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. A transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size)) β Sequence of hidden-states at the output of the last layer of the model.
pooler_output (jnp.ndarray of shape (batch_size, hidden_size)) β Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The FlaxRobertaPreTrainedModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, FlaxRobertaModel
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
>>> model = FlaxRobertaModel.from_pretrained("FacebookAI/roberta-base")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
>>> outputs = model(**inputs)
>>> last_hidden_states = outputs.last_hidden_state FlaxRobertaForCausalLM class transformers.FlaxRobertaForCausalLM < source >
( config: RobertaConfig input_shape: typing.Tuple = (1, 1) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True gradient_checkpointing: bool = False **kwargs )
Parameters
Roberta Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for autoregressive tasks.
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a flax.linen.Module subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
__call__ < source >( input_ids attention_mask = None token_type_ids = None position_ids = None head_mask = None encoder_hidden_states = None encoder_attention_mask = None params: dict = None dropout_rng: <function PRNGKey at 0x7fc27ba0dd80> = None train: bool = False output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None past_key_values: dict = None ) β transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or tuple(torch.FloatTensor)
Parameters
numpy.ndarray of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
numpy.ndarray of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
numpy.ndarray of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
numpy.ndarray of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]. numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:
bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. A transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
logits (jnp.ndarray of shape (batch_size, sequence_length, config.vocab_size)) β Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
cross_attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Cross attentions weights after the attention softmax, used to compute the weighted average in the cross-attention heads.
past_key_values (tuple(tuple(jnp.ndarray)), optional, returned when use_cache=True is passed or when config.use_cache=True) β Tuple of jnp.ndarray tuples of length config.n_layers, with each tuple containing the cached key, value states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting. Only relevant if config.is_decoder = True.
Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.
The FlaxRobertaPreTrainedModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, FlaxRobertaForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
>>> model = FlaxRobertaForCausalLM.from_pretrained("FacebookAI/roberta-base")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
>>> outputs = model(**inputs)
>>>
>>> next_token_logits = outputs.logits[:, -1] FlaxRobertaForMaskedLM class transformers.FlaxRobertaForMaskedLM < source >
( config: RobertaConfig input_shape: typing.Tuple = (1, 1) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True gradient_checkpointing: bool = False **kwargs )
Parameters
RoBERTa Model with a language modeling head on top.
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a flax.linen.Module subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
__call__ < source >( input_ids attention_mask = None token_type_ids = None position_ids = None head_mask = None encoder_hidden_states = None encoder_attention_mask = None params: dict = None dropout_rng: <function PRNGKey at 0x7fc27ba0dd80> = None train: bool = False output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None past_key_values: dict = None ) β transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling or tuple(torch.FloatTensor)
Parameters
numpy.ndarray of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
numpy.ndarray of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
numpy.ndarray of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
numpy.ndarray of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]. numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:
bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. A transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
last_hidden_state (jnp.ndarray of shape (batch_size, sequence_length, hidden_size)) β Sequence of hidden-states at the output of the last layer of the model.
pooler_output (jnp.ndarray of shape (batch_size, hidden_size)) β Last layer hidden-state of the first token of the sequence (classification token) further processed by a Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence prediction (classification) objective during pretraining.
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The FlaxRobertaPreTrainedModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, FlaxRobertaForMaskedLM
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
>>> model = FlaxRobertaForMaskedLM.from_pretrained("FacebookAI/roberta-base")
>>> inputs = tokenizer("The capital of France is [MASK].", return_tensors="jax")
>>> outputs = model(**inputs)
>>> logits = outputs.logits FlaxRobertaForSequenceClassification class transformers.FlaxRobertaForSequenceClassification < source >
( config: RobertaConfig input_shape: typing.Tuple = (1, 1) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True gradient_checkpointing: bool = False **kwargs )
Parameters
Roberta Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a flax.linen.Module subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
__call__ < source >( input_ids attention_mask = None token_type_ids = None position_ids = None head_mask = None encoder_hidden_states = None encoder_attention_mask = None params: dict = None dropout_rng: <function PRNGKey at 0x7fc27ba0dd80> = None train: bool = False output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None past_key_values: dict = None ) β transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or tuple(torch.FloatTensor)
Parameters
numpy.ndarray of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
numpy.ndarray of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
numpy.ndarray of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
numpy.ndarray of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]. numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:
bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. A transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
logits (jnp.ndarray of shape (batch_size, config.num_labels)) β Classification (or regression if config.num_labels==1) scores (before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The FlaxRobertaPreTrainedModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, FlaxRobertaForSequenceClassification
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
>>> model = FlaxRobertaForSequenceClassification.from_pretrained("FacebookAI/roberta-base")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
>>> outputs = model(**inputs)
>>> logits = outputs.logits FlaxRobertaForMultipleChoice class transformers.FlaxRobertaForMultipleChoice < source >
( config: RobertaConfig input_shape: typing.Tuple = (1, 1) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True gradient_checkpointing: bool = False **kwargs )
Parameters
Roberta Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a flax.linen.Module subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
__call__ < source >( input_ids attention_mask = None token_type_ids = None position_ids = None head_mask = None encoder_hidden_states = None encoder_attention_mask = None params: dict = None dropout_rng: <function PRNGKey at 0x7fc27ba0dd80> = None train: bool = False output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None past_key_values: dict = None ) β transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput or tuple(torch.FloatTensor)
Parameters
numpy.ndarray of shape (batch_size, num_choices, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
numpy.ndarray of shape (batch_size, num_choices, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
numpy.ndarray of shape (batch_size, num_choices, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
numpy.ndarray of shape (batch_size, num_choices, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]. numpy.ndarray of shape (batch_size, num_choices, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:
bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. A transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
logits (jnp.ndarray of shape (batch_size, num_choices)) β num_choices is the second dimension of the input tensors. (see input_ids above).
Classification scores (before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The FlaxRobertaPreTrainedModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, FlaxRobertaForMultipleChoice
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
>>> model = FlaxRobertaForMultipleChoice.from_pretrained("FacebookAI/roberta-base")
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> choice0 = "It is eaten with a fork and a knife."
>>> choice1 = "It is eaten while held in the hand."
>>> encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors="jax", padding=True)
>>> outputs = model(**{k: v[None, :] for k, v in encoding.items()})
>>> logits = outputs.logits FlaxRobertaForTokenClassification class transformers.FlaxRobertaForTokenClassification < source >
( config: RobertaConfig input_shape: typing.Tuple = (1, 1) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True gradient_checkpointing: bool = False **kwargs )
Parameters
Roberta Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a flax.linen.Module subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
__call__ < source >( input_ids attention_mask = None token_type_ids = None position_ids = None head_mask = None encoder_hidden_states = None encoder_attention_mask = None params: dict = None dropout_rng: <function PRNGKey at 0x7fc27ba0dd80> = None train: bool = False output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None past_key_values: dict = None ) β transformers.modeling_flax_outputs.FlaxTokenClassifierOutput or tuple(torch.FloatTensor)
Parameters
numpy.ndarray of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
numpy.ndarray of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
numpy.ndarray of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
numpy.ndarray of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]. numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:
bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. A transformers.modeling_flax_outputs.FlaxTokenClassifierOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
logits (jnp.ndarray of shape (batch_size, sequence_length, config.num_labels)) β Classification scores (before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The FlaxRobertaPreTrainedModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, FlaxRobertaForTokenClassification
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
>>> model = FlaxRobertaForTokenClassification.from_pretrained("FacebookAI/roberta-base")
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="jax")
>>> outputs = model(**inputs)
>>> logits = outputs.logits FlaxRobertaForQuestionAnswering class transformers.FlaxRobertaForQuestionAnswering < source >
( config: RobertaConfig input_shape: typing.Tuple = (1, 1) seed: int = 0 dtype: dtype = <class 'jax.numpy.float32'> _do_init: bool = True gradient_checkpointing: bool = False **kwargs )
Parameters
Roberta Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute span start logits and span end logits).
This model inherits from FlaxPreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading, saving and converting weights from PyTorch models)
This model is also a flax.linen.Module subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
__call__ < source >( input_ids attention_mask = None token_type_ids = None position_ids = None head_mask = None encoder_hidden_states = None encoder_attention_mask = None params: dict = None dropout_rng: <function PRNGKey at 0x7fc27ba0dd80> = None train: bool = False output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None return_dict: typing.Optional[bool] = None past_key_values: dict = None ) β transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput or tuple(torch.FloatTensor)
Parameters
numpy.ndarray of shape (batch_size, sequence_length)) β Indices of input sequence tokens in the vocabulary.
Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.
numpy.ndarray of shape (batch_size, sequence_length), optional) β Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:
numpy.ndarray of shape (batch_size, sequence_length), optional) β Segment token indices to indicate first and second portions of the inputs. Indices are selected in [0, 1]:
numpy.ndarray of shape (batch_size, sequence_length), optional) β Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]. numpy.ndarray of shape (batch_size, sequence_length), optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in [0, 1]`:
bool, optional) β Whether or not to return a ModelOutput instead of a plain tuple. A transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (RobertaConfig) and inputs.
start_logits (jnp.ndarray of shape (batch_size, sequence_length)) β Span-start scores (before SoftMax).
end_logits (jnp.ndarray of shape (batch_size, sequence_length)) β Span-end scores (before SoftMax).
hidden_states (tuple(jnp.ndarray), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) β Tuple of jnp.ndarray (one for the output of the embeddings + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (tuple(jnp.ndarray), optional, returned when output_attentions=True is passed or when config.output_attentions=True) β Tuple of jnp.ndarray (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.
The FlaxRobertaPreTrainedModel forward method, overrides the __call__ special method.
Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.
Example:
>>> from transformers import AutoTokenizer, FlaxRobertaForQuestionAnswering
>>> tokenizer = AutoTokenizer.from_pretrained("FacebookAI/roberta-base")
>>> model = FlaxRobertaForQuestionAnswering.from_pretrained("FacebookAI/roberta-base")
>>> question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet"
>>> inputs = tokenizer(question, text, return_tensors="jax")
>>> outputs = model(**inputs)
>>> start_scores = outputs.start_logits
>>> end_scores = outputs.end_logits< > Update on GitHub
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